Radiomic Model Associated with Tumor Microenvironment Predicts Immunotherapy Response and Prognosis in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma.

Journal: Research (Washington, D.C.)
Published Date: Jun 24, 2025

Abstract

No robust biomarkers have been identified to predict the efficacy of programmed cell death protein 1 (PD-1) inhibitors in patients with locoregionally advanced nasopharyngeal carcinoma (LANPC). We aimed to develop radiomic models using pre-immunotherapy MRI to predict the response to PD-1 inhibitors and the patient prognosis. This study included 246 LANPC patients (training cohort, = 117; external test cohort, = 129) from 10 centers. The best-performing machine learning classifier was employed to create the radiomic models. A combined model was constructed by integrating clinical and radiomic data. A radiomic interpretability study was performed with whole slide images (WSIs) stained with hematoxylin and eosin (H&E) and immunohistochemistry (IHC). A total of 150 patient-level nuclear morphological features (NMFs) and 12 cell spatial distribution features (CSDFs) were extracted from WSIs. The correlation between the radiomic and pathological features was assessed using Spearman correlation analysis. The radiomic model outperformed the clinical and combined models in predicting treatment response (area under the curve: 0.760 vs. 0.559 vs. 0.652). For overall survival estimation, the combined model performed comparably to the radiomic model but outperformed the clinical model (concordance index: 0.858 vs. 0.812 vs. 0.664). Six treatment response-related radiomic features correlated with 50 H&E-derived (146 pairs, ||= 0.31 to 0.46) and 2 to 26 IHC-derived NMF, particularly for CD45RO (69 pairs, ||= 0.31 to 0.48), CD8 (84, ||= 0.30 to 0.59), PD-L1 (73, ||= 0.32 to 0.48), and CD163 (53, || = 0.32 to 0.59). Eight prognostic radiomic features correlated with 11 H&E-derived (16 pairs, ||= 0.48 to 0.61) and 2 to 31 IHC-derived NMF, particularly for PD-L1 (80 pairs, ||= 0.44 to 0.64), CD45RO (65, ||= 0.42 to 0.67), CD19 (35, ||= 0.44 to 0.58), CD66b (61, || = 0.42 to 0.67), and FOXP3 (21, || = 0.41 to 0.71). In contrast, fewer CSDFs exhibited correlations with specific radiomic features. The radiomic model and combined model are feasible in predicting immunotherapy response and outcomes in LANPC patients. The radiology-pathology correlation suggests a potential biological basis for the predictive models.

Authors

  • Jie Sun
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China.
  • Xuewei Wu
    Department of Radiology, the First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.
  • Xiao Zhang
    Merck & Co., Inc., Rahway, NJ, USA.
  • Weiyuan Huang
    Department of Radiology, Hainan Affiliated Hospital of Hainan Medical University (Hainan General Hospital), Haikou, Hainan, China.
  • Xi Zhong
    Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
  • Xueyan Li
    College of Electronic Science and Engineering, Jilin University, Changchun, China. Electronic address: leexy@jlu.edu.cn.
  • Kaiming Xue
    Department of Radiology, The Third Bethune Hospital of Jilin University, Changchun, Jilin, China.
  • Shuyi Liu
    The Experimental High School Attached to Beijing Normal University, No. 14 Erlong Road, Beijing 100051, PR China.
  • Xianjie Chen
    Department of Cardiovascular Surgery, Henan Province People's Hospital, Fuwai Central China Cardiovascular Hospital, Henan Cardiovascular Hospital and Zhengzhou University, Zhengzhou, China.
  • Wenzhu Li
    Department of Radiology, Hainan Affiliated Hospital of Hainan Medical University (Hainan General Hospital), Haikou, Hainan, China.
  • Xin Liu
    Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences, Weifang, Shandong, China.
  • Hui Shen
    College of Mechatronics and Automation, National University of Defense Technology, Changsha, China.
  • Jingjing You
    Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China.
  • WenLe He
    Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.
  • Zhe Jin
    Zhejiang University, College of Computer Science and Technology, Hangzhou, China.
  • Lijuan Yu
    Center of PET/CT, The Third Affiliated Hospital of Harbin Medical University, The Affiliated Tumor Hospital of Harbin Medical University, Harbin, China. Electronic address: yulijuan2003@126.com.
  • Yuange Li
    Radiology Imaging Center, The Affiliated Hospital of Guangdong Medical University, 524001, Zhanjiang, Guangdong Province, P. R. China.
  • Shuixing Zhang
    Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, PR China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, Guangdong, PR China. Electronic address: shui7515@126.com.
  • Bin Zhang
    Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals